Weekly Digest — 2025-W32

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Diffusion Large Language Models (DLLMs) are emerging as a powerful alternative to the dominant Autoregressive Large Language Models, offering efficient parallel generation and capable global context modeling. However, the practical application of DLLMs is hindered by a critical architectural constraint: the need for a statically predefined generation length. This static length allocation leads to a problematic trade-off: insufficient lengths cripple performance on complex tasks, while excessive lengths incur significant computational overhead and sometimes result in performance degradation. While the inference framework is rigid, we observe that the model itself possesses internal signals that correlate with the optimal response length for a given task. To bridge this gap, we leverage these latent signals and introduce DAEDAL, a novel training-free denoising strategy that enables Dynamic Adaptive Length Expansion for Diffusion Large Language Models. DAEDAL operates in two phases: 1) Before the denoising process, DAEDAL starts from a short initial length and iteratively expands it to a coarse task-appropriate length, guided by a sequence completion metric. 2) During the denoising process, DAEDAL dynamically intervenes by pinpointing and expanding insufficient generation regions through mask token insertion, ensuring the final output is fully developed. Extensive experiments on DLLMs demonstrate that DAEDAL achieves performance comparable, and in some cases superior, to meticulously tuned fixed-length baselines, while simultaneously enhancing computational efficiency by achieving a higher effective token ratio. By resolving the static length constraint, DAEDAL unlocks new potential for DLLMs, bridging a critical gap with their Autoregressive counterparts and paving the way for more efficient and capable generation.

The current success of diffusion transformers heavily depends on the compressed latent space shaped by the pre-trained variational autoencoder(VAE). However, this two-stage training paradigm inevitably introduces accumulated errors and decoding artifacts. To address the aforementioned problems, researchers return to pixel space at the cost of complicated cascade pipelines and increased token complexity. In contrast to their efforts, we propose to model the patch-wise decoding with neural field and present a single-scale, single-stage, efficient, end-to-end solution, coined as pixel neural field diffusion~(PixelNerd). Thanks to the efficient neural field representation in PixNerd, we directly achieved 2.15 FID on ImageNet 256times256 and 2.84 FID on ImageNet 512times512 without any complex cascade pipeline or VAE. We also extend our PixNerd framework to text-to-image applications. Our PixNerd-XXL/16 achieved a competitive 0.73 overall score on the GenEval benchmark and 80.9 overall score on the DPG benchmark.

General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reasoning, web interaction, coding, and autonomous research capabilities. However, current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools, limiting accessibility and reproducibility for the research community. In this work, we present Cognitive Kernel-Pro, a fully open-source and (to the maximum extent) free multi-module agent framework designed to democratize the development and evaluation of advanced AI agents. Within Cognitive Kernel-Pro, we systematically investigate the curation of high-quality training data for Agent Foundation Models, focusing on the construction of queries, trajectories, and verifiable answers across four key domains: web, file, code, and general reasoning. Furthermore, we explore novel strategies for agent test-time reflection and voting to enhance agent robustness and performance. We evaluate Cognitive Kernel-Pro on GAIA, achieving state-of-the-art results among open-source and free agents. Notably, our 8B-parameter open-source model surpasses previous leading systems such as WebDancer and WebSailor, establishing a new performance standard for accessible, high-capability AI agents. Code is available at https://github.com/Tencent/CognitiveKernel-Pro

Large vision-language models (VLMs) have made significant strides in 2D visual understanding tasks, sparking interest in extending these capabilities to 3D scene understanding. However, current 3D VLMs often struggle with robust reasoning and generalization due to limitations in high-quality spatial data and the static nature of viewpoint assumptions. To address these challenges, we propose 3D-R1, a foundation model that enhances the reasoning capabilities of 3D VLMs. Specifically, we first construct a high-quality synthetic dataset with CoT, named Scene-30K, leveraging existing 3D-VL datasets and a data engine based on Gemini 2.5 Pro. It serves as cold-start initialization data for 3D-R1. Moreover, we leverage RLHF policy such as GRPO in the reinforcement learning training process to enhance reasoning capabilities and introduce three reward functions: a perception reward, a semantic similarity reward and a format reward to maintain detection accuracy and answer semantic precision. Furthermore, we introduce a dynamic view selection strategy that adaptively chooses the most informative perspectives for 3D scene understanding. Extensive experiments demonstrate that 3D-R1 delivers an average improvement of 10% across various 3D scene benchmarks, highlighting its effectiveness in enhancing reasoning and generalization in 3D scene understanding. Code: https://github.com/AIGeeksGroup/3D-R1. Website: https://aigeeksgroup.github.io/3D-R1.

Recent advances in large language model (LLM) agents have shown remarkable progress in software issue resolution, leveraging advanced techniques such as multi-agent collaboration and Monte Carlo Tree Search (MCTS). However, current agents act as memoryless explorers - treating each problem separately without retaining or reusing knowledge from previous repair experiences. This leads to redundant exploration of failed trajectories and missed chances to adapt successful issue resolution methods to similar problems. To address this problem, we introduce SWE-Exp, an experience - enhanced approach that distills concise and actionable experience from prior agent trajectories, enabling continuous learning across issues. Our method introduces a multi-faceted experience bank that captures both successful and failed repair attempts. Specifically, it extracts reusable issue resolution knowledge at different levels - from high-level problem comprehension to specific code changes. Experiments show that SWE-Exp achieves state-of-the-art resolution rate (41.6% Pass@1) on SWE-bench-Verified under open-source agent frameworks. Our approach establishes a new paradigm in which automated software engineering agents systematically accumulate and leverage repair expertise, fundamentally shifting from trial-and-error exploration to strategic, experience-driven issue resolution.

Multimodal referring segmentation aims to segment target objects in visual scenes, such as images, videos, and 3D scenes, based on referring expressions in text or audio format. This task plays a crucial role in practical applications requiring accurate object perception based on user instructions. Over the past decade, it has gained significant attention in the multimodal community, driven by advances in convolutional neural networks, transformers, and large language models, all of which have substantially improved multimodal perception capabilities. This paper provides a comprehensive survey of multimodal referring segmentation. We begin by introducing this field's background, including problem definitions and commonly used datasets. Next, we summarize a unified meta architecture for referring segmentation and review representative methods across three primary visual scenes, including images, videos, and 3D scenes. We further discuss Generalized Referring Expression (GREx) methods to address the challenges of real-world complexity, along with related tasks and practical applications. Extensive performance comparisons on standard benchmarks are also provided. We continually track related works at https://github.com/henghuiding/Awesome-Multimodal-Referring-Segmentation.

We present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address the challenges of complex text rendering, we design a comprehensive data pipeline that includes large-scale data collection, filtering, annotation, synthesis, and balancing. Moreover, we adopt a progressive training strategy that starts with non-text-to-text rendering, evolves from simple to complex textual inputs, and gradually scales up to paragraph-level descriptions. This curriculum learning approach substantially enhances the model's native text rendering capabilities. As a result, Qwen-Image not only performs exceptionally well in alphabetic languages such as English, but also achieves remarkable progress on more challenging logographic languages like Chinese. To enhance image editing consistency, we introduce an improved multi-task training paradigm that incorporates not only traditional text-to-image (T2I) and text-image-to-image (TI2I) tasks but also image-to-image (I2I) reconstruction, effectively aligning the latent representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and reconstructive representations, respectively. This dual-encoding mechanism enables the editing module to strike a balance between preserving semantic consistency and maintaining visual fidelity. Qwen-Image achieves state-of-the-art performance, demonstrating its strong capabilities in both image generation and editing across multiple benchmarks.

Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units for retrieval. However, due to dependencies across the original document, contextual information is often essential for accurately interpreting each chunk. To address this, prior work has explored encoding longer context windows to produce embeddings for longer chunks. Despite these efforts, gains in retrieval and downstream tasks remain limited. This is because (1) longer chunks strain the capacity of embedding models due to the increased amount of information they must encode, and (2) many real-world applications still require returning localized evidence due to constraints on model or human bandwidth. We propose an alternative approach to this challenge by representing short chunks in a way that is conditioned on a broader context window to enhance retrieval performance -- i.e., situating a chunk's meaning within its context. We further show that existing embedding models are not well-equipped to encode such situated context effectively, and thus introduce a new training paradigm and develop the situated embedding models (SitEmb). To evaluate our method, we curate a book-plot retrieval dataset specifically designed to assess situated retrieval capabilities. On this benchmark, our SitEmb-v1 model based on BGE-M3 substantially outperforms state-of-the-art embedding models, including several with up to 7-8B parameters, with only 1B parameters. Our 8B SitEmb-v1.5 model further improves performance by over 10% and shows strong results across different languages and several downstream applications.

Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantities such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. Here, we introduce CellForge, an agentic system that leverages a multi-agent framework that transforms presented biological datasets and research objectives directly into optimized computational models for virtual cells. More specifically, given only raw single-cell multi-omics data and task descriptions as input, CellForge outputs both an optimized model architecture and executable code for training virtual cell models and inference. The framework integrates three core modules: Task Analysis for presented dataset characterization and relevant literature retrieval, Method Design, where specialized agents collaboratively develop optimized modeling strategies, and Experiment Execution for automated generation of code. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus. We demonstrate CellForge's capabilities in single-cell perturbation prediction, using six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities. CellForge consistently outperforms task-specific state-of-the-art methods. Overall, CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge. Our code is publicly available at https://github.com/gersteinlab/CellForge.

Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpose cybersecurity data, representational complexity, and safety and regulatory concerns. To address this gap, we previously introduced Foundation-Sec-8B, a cybersecurity-focused LLM suitable for fine-tuning on downstream tasks. That model, however, was not designed for chat-style interactions or instruction-following. In this report, we release Foundation-Sec-8B-Instruct: a model specifically trained for general-purpose cybersecurity dialogue. Built on Foundation-Sec-8B, it combines domain-specific knowledge with instruction-following, conversational capabilities, and alignment with human preferences to produce high-quality, relevant responses. Comprehensive evaluations show that Foundation-Sec-8B-Instruct outperforms Llama 3.1-8B-Instruct on a range of cybersecurity tasks while matching its instruction-following performance. It is also competitive with GPT-4o-mini on cyber threat intelligence and instruction-following tasks. We envision Foundation-Sec-8B-Instruct becoming an indispensable assistant in the daily workflows of cybersecurity professionals. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct.

Reasoning models excel in complex problem solving but exhibit a concerning trade off between reasoning capabilities and instruction following abilities. Existing approaches for improving instruction following rely on stronger external models, creating methodological bottlenecks and practical limitations including increased costs and accessibility constraints. We propose a self-supervised RL framework that leverages reasoning models' own internal signals to improve instruction following capabilities without external supervision. Extensive experiments demonstrate that our framework significantly improves instruction following capabilities while maintaining reasoning performance, offering a scalable and cost-effective approach to enhance instruction following in reasoning models. The data and code are publicly available at https://github.com/Rainier-rq/verl-if.

Recent advances in large language models (LLMs) have driven impressive progress in omni-modal understanding and generation. However, training omni-modal LLMs remains a significant challenge due to the heterogeneous model architectures required to process diverse modalities, necessitating sophisticated system design for efficient large-scale training. Existing frameworks typically entangle model definition with parallel logic, incurring limited scalability and substantial engineering overhead for end-to-end omni-modal training. % We present \veomni, a modular and efficient training framework to accelerate the development of omni-modal LLMs. \veomni introduces model-centric distributed recipes that decouples communication from computation, enabling efficient 3D parallelism on omni-modal LLMs. \veomni also features a flexible configuration interface supporting seamless integration of new modalities with minimal code change. % Using \veomni, a omni-modal mixture-of-experts (MoE) model with 30B parameters can be trained with over 2,800 tokens/sec/GPU throughput and scale to 160K context lengths via 3D parallelism on 128 GPUs, showcasing its superior efficiency and scalability for training large omni-modal LLMs.

俄罗斯航天局载人航天计划执行主任 Sergey Krikalev 承认，国际空间站（ISS）上的俄罗斯舱仍然在漏气。漏气最早是于 2019 年发现的，尽管多次确定漏气位置和进行修复，但国际空间站仍在漏气。空间站上驻扎的宇航员目前没有生命危险，但老化结构中裂缝的情况仍然不能令人满意。目前漏气有所减少，但仍在持续。俄罗斯和美国的科学家正努力解决该问题，追根究底，确保空间站未来不会再次发生类似事件。

Valve 公布的 2025 年 7 月 Steam 硬件和软件调查显示，玩家所用操作系统中 Linux 比例接近 3% 达到 2.89%（增加 0.32%），Windows 减少 0.44% 占 95.23%，OSX 占 1.88%。Linux 玩家的比例接近历史高点，这一趋势主要受到掌机 Steam Deck 的推动。在 PC 处理器中英特尔 CPU 减少 0.75% 跌至 60% 以内占 59.52%，AMD CPU 增加 0.74% 占 40.39%。对于用户使用的语言，简体中文减少 1.29% 占 25.44%，英文占 37.70%。

如果一所大学的研究人员发表的论文大量撤稿，印度国家大学排名将会对将该大学进行惩罚。此举旨在遏制日益增多的因科学不端行为而导致论文撤稿的问题。论文撤稿一部分是因为无意造成的错误，但还有一部分是因为有意的不端行为。根据 Retraction Watch 对过去 30 年撤稿数据库的分析，印度的撤稿论文数量仅次于中国和美国。美国每发表 1000 篇论文中只有不到 1 篇被撤稿，中国每发表 1000 篇论文中有逾 3 篇被撤稿，而印度是每发表 1000 篇论文有 2 篇被撤稿。印度和中国的论文撤稿大部分是因为科学不端行为或科学诚信问题。

比利时布鲁塞尔商事法庭发布了一份禁令，旨在限制对影子图书馆的访问，受影响的网站包括安娜的档案 (Anna's Archive)、Libgen、OceanofPDF、Z-Library 以及互联网档案馆的 Open Library。除了 ISP，搜索引擎、DNS 解析器、广告商、域名服务商、内容分发网络 (CDN) 和托管商都需要采取行动限制对上述网站的访问。Open Library 由已故的 Aaron Swartz 和互联网档案馆创始人 Brewster Kahle 等人创办，旨在存档所有已出版书籍，允许读者在线借阅。与其它电子图书馆类似，它的每本书每次只能借出一份拷贝。但不同之处是它的电子书没有获得授权，而是通过自己扫描去创建电子版。

搜索巨人去年宣布，它将于 2025 年 8 月 25 日关闭 Google URL Shortener 短链接服务（goo.gl/*），届时所有 goo.gl 链接将会停止响应。距离关闭日期不到一个月时间，在依赖于 goo.gl 短链接的开发者、教育工作者和记者等表达担忧之后，Google 改变了主意，采取了更温和的立场：它将只禁用自 2024 年底以来没有任何活动的 goo.gl 链接，如果 goo.gl 链接在活跃使用或点击，这些链接将能继续使用。

2025 年 2 月，Hannah Cairo 在预印本平台 arxiv 上发表了一篇论文，解决了有 40 年历史的 Mizohata-Takeuchi 猜想，她年仅 17 岁，主要依靠自学，一时间震惊了数学界。Cairo 证明该猜想是错误的。她在巴哈马的 Nassau 长大，父亲是程序员，在这里获得了一份工作，因此一家人搬来这里。她还有一位大三岁的哥哥和小八岁的弟弟。在巴拿马他们都是在家中学习。Cairo 通过 Khan Academy 的在线课程学习数学，到她 11 岁时已经读完了微积分课程。父母为她找了几位数学教授远程辅导，她大部分时间仍然是自学，以至于其中一位教授、Clark 大学的 Amir Aazami 认为收钱有愧。到 14 岁时她已经读完了本科高年级数学课程。2021 年由于新冠疫情，一家人困住在芝加哥的祖父母家。这对她反而是好事，她开始扩大数学圈，接触越来越多的同行。2023 年，她申请了多数大学，但由于没有读完高中很多大学都拒绝了。她跟着哥哥去了加州伯克利，选修高等数学课程，其中一门是关于傅里叶限制理论（Fourier restriction theory）的研究生课程，授课老师是张瑞祥。几周后张瑞祥布置了一道 Mizohata-Takeuchi 猜想的简化版本作为作业，此举主要是鼓励学生探索数学领域的高级技巧。她完成了习题，在张的鼓励下进一步探索。她构造了一个函数否定了 Mizohata-Takeuchi 猜想。在完成证明之后，她决定跳过大学阶段，直接读数学博士。由于没有读完大学，她申请的多所大学也拒绝了，只有马里兰大学和约翰霍普金斯大学愿意录取，她选择了马里兰大学，将从秋天开始入学，当她完成学业，这将是她的第一个学位。

日本京都大学的科学家研发出一种效力与吗啡相当但无严重副作用的止痛药。吗啡常被癌症患者使用，它有呼吸困难和成瘾等严重副作用。新药物 Adrian 的工作原理与吗啡和现有的合成阿片类药物完全不同，研究团队声称有望彻底改变医学领域的疼痛控制，有助于解决阿片类药物滥用问题。当人遭遇危及生命的情况时，大脑会分泌去甲肾上腺素(norepinephrine)去抑制疼痛。新研究集中在是人体调节去甲肾上腺素过度分泌的机制。研究团队通过引入新技术首次成功研发出一种能阻断这种调控的药物。科学家计划 2026 年在美国开展临床试验，2028 年投入实用。

科学家理解大脑运作主要使用两种工具，它们都有各自的优点和缺点：脑电图 (EEG)廉价且轻便，但无法读取大脑外皮层之外的信息；功能性核磁共振成像 (fMRI) 昂贵且体积庞大但可以深入大脑。现在格拉斯哥(Glasgow)大学研究团队找到了一种能集两者于一身的技术：像 EEG 那样廉价且轻便，像 fMRI 那样能读取大脑深层的信息。他们使用激光器从大脑一侧发射数以百万的光子，然后测量到达另一侧的时间。由于只有极少数光子能完全穿过大脑，因此研究的一大难点是降低背景噪音。这项技术离真正实用还有一段距离，研究人员还需要克服更多障碍。

英国科学家发现，超加工饮食对减重和降低心血管代谢疾病风险的效果可能不如最少加工的饮食，即使这两种饮食都遵循相同的国家饮食指南。研究结果基于一项对英国 55 名成年人开展的社群水平的临床试验，揭示了在整体营养构成之外，食品加工程度对特定健康结局的可能影响。全球超加工食物消耗量在近几十年里快速增加，而肥胖症以及2型糖尿病和心血管疾病这类慢性病的发病率也在同期上升。研究人员开展了一项随机交叉试验，比较了以超加工食品为主和以最少加工食品为主的饮食，两种饮食结构都遵循了英国《健康饮食指南》——一组促进健康均衡营养的国家饮食建议。试验中的 55 名成人或接受预制的超加工食品，如早餐谷物或即食千层意面；或接受预制的最少加工食品，如隔夜燕麦或自制肉酱意面，这些食品在 8 周内分别配送到家。休息 4 周后，受试者换成另一种饮食再继续 8 周，从而能在受试者本人身上比较超加工食品和最少加工食品在 6 个月期间的影响。50 名受试者至少完成了一种饮食。研究者发现，遵循英国《健康饮食指南》的两种饮食都能在 8 周内显著减重。不过，最少加工饮食的平均减重量为 2%，而超加工饮食只有 1%。除了减重，最少加工饮食能更有效地改善与心血管代谢健康指标相关的身体成分，如降低脂肪总量、内脏脂肪和甘油三酯水平，但超加工饮食后的低密度脂蛋白胆固醇更低。

陪审团上周裁决特斯拉对一起牵涉到 Autopilot 的车祸过失死亡事件负有部分责任。庭审记录显示，特斯拉试图将所有责任都归罪于司机，主动隐瞒 Autopilot 在事故前后表现的关键证据。在车祸发生三分钟内，特斯拉汽车将碰撞快照（collision snapshot）——视频、CAN‑bus streams、EDR 数据等——上传到特斯拉公司的服务器上，然后删除了本地拷贝，使得特斯拉公司成为唯一一个能访问关键证据的实体。警方在多年之后才让特斯拉承认碰撞快照的存在。专家通过从车载电脑上取证恢复数据确认特斯拉一直拥有该“碰撞快照”。而特斯拉一直宣称快照数据并不存在。

就算没有母星相伴，部分质量与木星差不多的漂流行星，也可能孕育出属于自己的微型行星系统。这些流浪天体可能跟恒星一样，是从巨大气体分子云塌缩形成的；也有可能原本属于某个行星系统的成员，后来被其他大型行星的重力扰动踢出来，变成在星际空间中漂流的行星。研究团队运用韦伯望远镜上两套高灵敏度的红外线相机，从 2024年 8 月到 10 月间，对这些天体进行详细的光谱测量，并分析其结构与组成。结果显示它们的质量确实与木星相当，在其中 6 颗行星周围还发射出较为多量的红外线，显示它们身边环绕着温暖的气体尘埃圆盘，这正是行星系统形成时常见的特征。观测结果还发现这些尘埃盘中含有矽酸盐颗粒，不但有逐渐成长的迹象，还出现结晶化现象，这正是行星系统中形成岩石质行星形成的第一个步骤。过去只有在恒星或棕矮星周围的气体尘埃圆盘中发现这种现象，如今却首度在质量小得多，与木星质量相近的漂流行星中被侦测出来。

CDN 服务商 Cloudflare 指责 AI 搜索引擎公司 Perplexity 使用隐蔽策略绕过网站禁止抓取的指令。Cloudflare 称它收到了客户的投诉，客户通过 robots.txt 以及 Web 应用防火墙屏蔽了 Perplexity 的搜索爬虫，然而尽管采取了这些措施 Perplexity 的爬虫仍然继续访问网站内容。Cloudflare 随后展开了调查，发现当 Perplexity 注意到 robots.txt 或防火墙规则屏蔽其爬虫后，它会使用一个隐蔽的机器人爬虫，使用一系列策略掩盖其活动。此举意味着 Perplexity 违反了实施了 30 多年的互联网规范。